Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico

As clinicians and scientists gather more data on the clinical trajectory of COVID-19 and the biology of its causative agent, the SARS-CoV-2 virus, novel strategies are needed to integrate these data to inform new therapies. A recent study by Howell et al. introduces a network model of viral-host int...

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Autores principales: Diao, James A., Raza, Marium M., Venkatesh, Kaushik P., Kvedar, Joseph C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Editorial
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033809/
https://www.ncbi.nlm.nih.gov/pubmed/35459899
http://dx.doi.org/10.1038/s41746-022-00599-5
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author Diao, James A.
Raza, Marium M.
Venkatesh, Kaushik P.
Kvedar, Joseph C.
author_facet Diao, James A.
Raza, Marium M.
Venkatesh, Kaushik P.
Kvedar, Joseph C.
author_sort Diao, James A.
collection PubMed
description As clinicians and scientists gather more data on the clinical trajectory of COVID-19 and the biology of its causative agent, the SARS-CoV-2 virus, novel strategies are needed to integrate these data to inform new therapies. A recent study by Howell et al. introduces a network model of viral-host interactions to produce explainable and testable predictions for treatment effects. Their model was consistent with experimental data and recommended treatments, and one of its predicted drug combinations was validated through in vitro assays. These findings support the utility of computational strategies for leveraging the vast literature on COVID-19 to generate insights for drug repurposing.
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spelling pubmed-90338092022-04-28 Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico Diao, James A. Raza, Marium M. Venkatesh, Kaushik P. Kvedar, Joseph C. NPJ Digit Med Editorial As clinicians and scientists gather more data on the clinical trajectory of COVID-19 and the biology of its causative agent, the SARS-CoV-2 virus, novel strategies are needed to integrate these data to inform new therapies. A recent study by Howell et al. introduces a network model of viral-host interactions to produce explainable and testable predictions for treatment effects. Their model was consistent with experimental data and recommended treatments, and one of its predicted drug combinations was validated through in vitro assays. These findings support the utility of computational strategies for leveraging the vast literature on COVID-19 to generate insights for drug repurposing. Nature Publishing Group UK 2022-04-22 /pmc/articles/PMC9033809/ /pubmed/35459899 http://dx.doi.org/10.1038/s41746-022-00599-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Editorial
Diao, James A.
Raza, Marium M.
Venkatesh, Kaushik P.
Kvedar, Joseph C.
Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico
title Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico
title_full Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico
title_fullStr Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico
title_full_unstemmed Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico
title_short Computational drug repurposing in the age of COVID-19: mixing antiviral cocktails in silico
title_sort computational drug repurposing in the age of covid-19: mixing antiviral cocktails in silico
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033809/
https://www.ncbi.nlm.nih.gov/pubmed/35459899
http://dx.doi.org/10.1038/s41746-022-00599-5
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